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A Cyber-Physical Systems Approach to Energy Management in Data
CentersPresented by Chen He
Adopted form the paper authors
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Outline
Introduction Cyber-physical model Control approach Simulation results Discussion
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Motivation
Load 7GW peak power consumption in 2006(US) 12GW projected for 2011
Cost $4.5 billion for energy in 2006 Cost of electricity will soon exceed cost of
hardware
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Motivation Related Works
Server level Low-power states(eg. Sleep and hibernate modes),
Processor dynamic voltage and frequency scaling, DVFS and on/off states, resource redirection and task scheduling[3,5,7,8,11,15,21,22,23,24]
Data Center level Change workload placement to reduce A/C costs[12] Dynamic vary air flows to specific locations to improve
cooling efficiency[20] Tolia [28] proposed unified control of server power and
cooling , but in Intra-zone (blade server) level Can we create a comprehensive model to manage data
center level power consumption through unified control?
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Temperature distribution
Image: R.K. Sharma et al. “Balance of Power: Dynamic Thermal Management of Internet Data Center”,Jan. 2005 I
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Cyber-physical coupling
Workload type, execution, and allocation policies affect the cooling system power consumption Distinct workloads induce differences in server
power consumption Some locations in the data center are easier to
cool than others
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Cyber-physical coupling-Example Moving jobs(cyber)
from servers in zone A to servers in zone B How will the
temperature distribution change?
How will the performance change?
Will this lower the overall power consumption?
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Data center management problem
Find the best Job and resource allocation policies Cooling approach
In order to minimize the data center operating cost(power + performance), subject to Temperature constraints
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Outline
Introduction Cyber-physical model Control approach Simulation results Discussion
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Cyber-physical model
Computational network Event driven system(wl distribution,QoS)
Thermal network Time driven system(heat.e, p.c, h.p)
Coupling Server power consumption
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Computational network model Classed open queuing network
J job classes N nodes
It relates Job arrival rate: Available and used computational resources Server power consumption Quality of service (QoS) cost
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Computational network variables
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Job allocation model
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Server model
Servers are collections of computational resources
Assumptions Less allocated resources implies lower QoS Less allocated resources implies lower power
consumption values For each job class, server resources can be
represented by a scalar value
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Server power state Models available resources at a
server Concept similar to CPU power state
Lower clock frequence Slower job execution rate Lower power consumption
Defined over a finite, countable set For a computational node
Lower power state values Slower job execution rate Lower power consumption
Defined over the interval [0,1]
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Thermal network
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Thermal network variables
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Thermal server nodes
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CRAC units
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Environment Nods
Data center level model Neglect the power consumption of Environment
nodes. Zone level model
Model as same as thermal server node.
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Outline
Introduction Cyber-physical model Control approach Simulation results Discussion
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Control approach
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Data center level cost
Formula
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Data center level cost
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Outline
Introduction Cyber-physical model Control approach Simulation results Discussion
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Simulation
• Environment
• Job class:J=1; Thermal constraint: 5<T<25; power consumption is 3 cents/KWhr
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Simulation Coordinated (proposed MPC) Uncoordinated algorithm(seperated)
Find the best trade-off between server powering cost and QoS cost
Minimize CRAC power consumption Disregard thermal-computational coupling
Uniform algorithm(use all resource) Maximize QoS Fix CRAC reference temperatures in order to satisfy
thermal constraints for the worst case scenario
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Total cost over time
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Conclusions Workload execution and cooling system power
consumption are coupled Model and control approach have to consider both
computational and thermal characteristics of a data center
We proposed a model and a control strategy to realize the best trade-off between energy costs and quality of service Simulation results suggest a coordinated controller
can outperform other uncoordinated control
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Future research directions Our queueing model disregards job interaction
Is there a better model able to represent job interactions in a data center?
Proposed control strategy for realizing the best trade-off between satisfying user requests and energy consumption More research is needed to understand what factors
are most significant in determining the effectiveness of coordinated control
Which is the best way to aggregate nodes into single entity at higher hierarchy levels?
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Discussion
Contributions Shortcomings
Some coefficients come from single data center statistical results
Need more workload
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QoS Cost
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QoS=job execution rate-job arrival rate
[ ( ), ( )]QoS floor c celling c
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